Generative Deep Learning: Teaching Machines to Paint, Write, Compose, and Play
暫譯: 生成式深度學習：教導機器繪畫、寫作、作曲與演奏

Foster, David

Generative Deep Learning: Teaching Machines to Paint, Write, Compose, and Play

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商品描述

Generative modeling is one of the hottest topics in artificial intelligence. Recent advances in the field have shown how it's possible to teach a machine to excel at human endeavors--such as drawing, composing music, and completing tasks--by generating an understanding of how its actions affect its environment.

With this practical book, machine learning engineers and data scientists will learn how to recreate some of the most famous examples of generative deep learning models, such as variational autoencoders and generative adversarial networks (GANs). You'll also learn how to apply the techniques to your own datasets.

David Foster, cofounder of Applied Data Science, demonstrates the inner workings of each technique, starting with the basics of deep learning before advancing to the most cutting-edge algorithms in the field. Through tips and tricks, you'll learn how to make your models learn more efficiently and become more creative.

  • Get a fundamental overview of generative modeling
  • Learn how to use the Keras and TensorFlow libraries for deep learning
  • Discover how variational autoencoders (VAEs) work
  • Get practical examples of generative adversarial networks (GANs)
  • Understand how to build generative models that learn how to paint, write, and compose
  • Apply generative models within a reinforcement learning setting to accomplish tasks

商品描述(中文翻譯)

生成模型是人工智慧中最熱門的主題之一。該領域的最新進展顯示，如何教導機器在各種人類活動中表現出色，例如繪畫、作曲和完成任務，透過生成對其行為如何影響環境的理解。

在這本實用的書中，機器學習工程師和數據科學家將學習如何重現一些最著名的生成深度學習模型示例，例如變分自編碼器（variational autoencoders）和生成對抗網絡（generative adversarial networks, GANs）。您還將學習如何將這些技術應用於自己的數據集。

Applied Data Science 的共同創辦人 David Foster 展示了每種技術的內部運作，從深度學習的基本概念開始，然後進入該領域最前沿的算法。透過技巧和竅門，您將學會如何讓模型更有效地學習並變得更具創造力。

- 獲得生成模型的基本概述
- 學習如何使用 Keras 和 TensorFlow 庫進行深度學習
- 發現變分自編碼器（VAEs）的工作原理
- 獲得生成對抗網絡（GANs）的實用示例
- 理解如何構建能夠學習繪畫、寫作和作曲的生成模型
- 在強化學習環境中應用生成模型以完成任務

作者簡介

David Foster is the co-founder of Applied Data Science, a data science consultancy delivering bespoke solutions for clients. He holds an MA in Mathematics from Trinity College, Cambridge, UK and an MSc in Operational Research from the University of Warwick.

David has won several international machine learning competitions, including the Innocentive Predicting Product Purchase challenge and was awarded first prize for a visualisation that enables a pharmaceutical company in the US to optimize site selection for clinical trials.

He is an active participant in the online data science community and has authored several successful blog posts on deep reinforcement learning including 'How To Build Your Own AlphaZero AI'.

作者簡介(中文翻譯)

大衛·福斯特（David Foster）是應用數據科學（Applied Data Science）的共同創辦人，這是一家提供客製化解決方案的數據科學顧問公司。他擁有英國劍橋大學三一學院的數學碩士學位（MA in Mathematics）以及華威大學的運籌學碩士學位（MSc in Operational Research）。

大衛曾贏得多個國際機器學習競賽，包括Innocentive預測產品購買挑戰，並因其可視化作品獲得第一名，該作品幫助美國的一家製藥公司優化臨床試驗的地點選擇。

他是在線數據科學社群的活躍參與者，並撰寫了多篇成功的部落格文章，主題包括深度強化學習，其中一篇名為《如何建立自己的AlphaZero AI》。

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